The Silhouette Learning Network (SLN) aims to learn the appearance knowledge of humans from silhouettes that contain 2D spatial information like clothing and hairstyle.
The SLN has six convolutional layers which are similar to the backbone of GaitSet [1]}.
As is shown in Figure REF , the sequences of silhouettes are fed into a CNN.
We formulate \(X_{sil} = \lbrace  \mathbf {x}_i\rbrace _{i=1}^L\)  as the input sequence, where \( \mathbf {x}_i \in \mathbb {R}^{H \times W}\)  is the \(i\) -th binary frame, \(L\)  is the length of the sequence, H and W are the height and width of the silhouette image.
For a frame \(\mathbf {x}_i\) , the process can be formulated as:
\(\mathbf {F}_i = F(\mathbf {x}_i),\) 
